Evaluation of short-time fourier-transformation spectrograms derived from the vibration measurement of internal-combustion engines

Manhertz, Gábor; Modok, Daniel; Bereczky, Ákos

doi:10.1109/epepemc.2016.7752098

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…An analysis of spectrograms was proposed by Manhertz et al [23] for the analysis of laser-vibrometer measurements. Dominant frequencies were extracted for a four-cylinder engine during different operational conditions.…”

Section: Approach and Objectivementioning

confidence: 99%

Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades

Loss

Bergmann

2021

Applied Sciences

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Monitoring the structural health of wind turbine blades is essential to increase energy capture and operational safety of turbines, and therewith enhance competitiveness of wind energy. With the current trends of designing blades ever longer, detailed knowledge of the vibrational characteristics at any point along the blade is desirable. In our approach, we monitor vibrations during operation of the turbine by wirelessly measuring accelerations on the outside of the blades. We propose an algorithm to extract so-called vibration-based fingerprints from those measurements, i.e., dominant vibrations such as eigenfrequencies and narrow-band noise. These fingerprints can then be used for subsequent analysis and visualisation, e.g., for comparing fingerprints across several sensor positions and for identifying vibrations as global or local properties. In this study, data were collected by sensors on two test turbines and fingerprints were successfully extracted for vibrations with both low and high operational variability. An analysis of sensors on the same blade indicates that fingerprints deviate for positions at large radial distance or at different blade sides and, hence, an evaluation with larger datasets of sensors at different positions is promising. In addition, the results show that distributed measurements on the blades are needed to gain a detailed understanding of blade vibrations and thereby reduce loads, increase energy harvesting and improve future blade design. In doing so, our method provides a tool for analysing vibrations with relation to environmental and operational variability in a comprehensive manner.

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Section: Approach and Objectivementioning

confidence: 99%

Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades

Loss

Bergmann

2021

Applied Sciences

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“…Due to fast development of computational technologies and sensors, most of the prognostic and diagnostic systems in industries tend to use signal processing techniques. Existing data-driven methods or signal processing-based techniques include statistical methods [ 23 ], artificial neural networks (ANNs) [ 24 ] and other soft computing techniques [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Operational Modes Detection in Industrial Gas Turbines Using an Ensemble of Clustering Methods

Ghazvini

Sànchez–Marrè

Bahilo

et al. 2021

Sensors

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Operational modes of a process are described by a number of relevant features that are indicative of the state of the process. Hundreds of sensors continuously collect data in industrial systems, which shows how the relationship between different variables changes over time and identifies different modes of operation. Gas turbines’ operational modes are usually defined regarding their expected energy production, and most research works either are focused a priori on obtaining these modes solely based on one variable, the active load, or assume a fixed number of states and build up predictive models to classify new situations as belonging to the predefined operational modes. However, in this work, we take into account all available parameters based on sensors’ data because other factors can influence the system status, leading to the identification of a priori unknown operational modes. Furthermore, for gas turbine management, a key issue is to detect these modes using a real-time monitoring system. Our approach is based on using unsupervised machine learning techniques, specifically an ensemble of clusters to discover consistent clusters, which group data into similar groups, and to generate in an automatic way their description. This description, upon interpretation by experts, becomes identified and characterized as operational modes of an industrial process without any kind of a priori bias of what should be the operational modes obtained. Our proposed methodology can discover and identify unknown operational modes through data-driven models. The methodology was tested in our case study with Siemens gas turbine data. From available sensors’ data, clusters descriptions were obtained in an automatic way from aggregated clusters. They improved the quality of partitions tuning one consistency parameter and excluding outlier clusters by defining filtering thresholds. Finally, operational modes and/or sub-operational modes were identified with the interpretation of the clusters description by process experts, who evaluated the results very positively.

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Multimodal Lung Disease Classification using Deep Convolutional Neural Network

Tariq

Shah

Lee

2020

2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Evaluation of short-time fourier-transformation spectrograms derived from the vibration measurement of internal-combustion engines

Cited by 6 publications

References 15 publications

Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades

Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades

Operational Modes Detection in Industrial Gas Turbines Using an Ensemble of Clustering Methods

Multimodal Lung Disease Classification using Deep Convolutional Neural Network

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